# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

"""
Functions for benchmarks.
"""

import numpy as np
import pprint
import torch
import tqdm
from fvcore.common.timer import Timer

import slowfast.utils.logging as logging
import slowfast.utils.misc as misc
from slowfast.datasets import loader
from slowfast.utils.env import setup_environment

logger = logging.get_logger(__name__)


def benchmark_data_loading(cfg):
    """
    Benchmark the speed of data loading in PySlowFast.
    Args:

        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    # Set up environment.
    setup_environment()
    # Set random seed from configs.
    np.random.seed(cfg.RNG_SEED)
    torch.manual_seed(cfg.RNG_SEED)

    # Setup logging format.
    logging.setup_logging(cfg.OUTPUT_DIR)

    # Print config.
    logger.info("Benchmark data loading with config:")
    logger.info(pprint.pformat(cfg))

    timer = Timer()
    dataloader = loader.construct_loader(cfg, "train")
    logger.info(
        "Initialize loader using {:.2f} seconds.".format(timer.seconds())
    )
    # Total batch size across different machines.
    batch_size = cfg.TRAIN.BATCH_SIZE * cfg.NUM_SHARDS
    log_period = cfg.BENCHMARK.LOG_PERIOD
    epoch_times = []
    # Test for a few epochs.
    for cur_epoch in range(cfg.BENCHMARK.NUM_EPOCHS):
        timer = Timer()
        timer_epoch = Timer()
        iter_times = []
        if cfg.BENCHMARK.SHUFFLE:
            loader.shuffle_dataset(dataloader, cur_epoch)
        for cur_iter, _ in enumerate(tqdm.tqdm(dataloader)):
            if cur_iter > 0 and cur_iter % log_period == 0:
                iter_times.append(timer.seconds())
                ram_usage, ram_total = misc.cpu_mem_usage()
                logger.info(
                    "Epoch {}: {} iters ({} videos) in {:.2f} seconds. "
                    "RAM Usage: {:.2f}/{:.2f} GB.".format(
                        cur_epoch,
                        log_period,
                        log_period * batch_size,
                        iter_times[-1],
                        ram_usage,
                        ram_total,
                    )
                )
                timer.reset()
        epoch_times.append(timer_epoch.seconds())
        ram_usage, ram_total = misc.cpu_mem_usage()
        logger.info(
            "Epoch {}: in total {} iters ({} videos) in {:.2f} seconds. "
            "RAM Usage: {:.2f}/{:.2f} GB.".format(
                cur_epoch,
                len(dataloader),
                len(dataloader) * batch_size,
                epoch_times[-1],
                ram_usage,
                ram_total,
            )
        )
        logger.info(
            "Epoch {}: on average every {} iters ({} videos) take {:.2f}/{:.2f} "
            "(avg/std) seconds.".format(
                cur_epoch,
                log_period,
                log_period * batch_size,
                np.mean(iter_times),
                np.std(iter_times),
            )
        )
    logger.info(
        "On average every epoch ({} videos) takes {:.2f}/{:.2f} "
        "(avg/std) seconds.".format(
            len(dataloader) * batch_size,
            np.mean(epoch_times),
            np.std(epoch_times),
        )
    )
